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Comparison of clustering approaches with application to dual colour protein data
Cells communicate with their environment via proteins, located at the plasma membrane separating the interior of a cell from its surroundings. The spatial distribution of these proteins in the plasma membrane under different physiological conditions is of importance, since this may influence their s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Institution of Engineering and Technology
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687232/ https://www.ncbi.nlm.nih.gov/pubmed/29337285 http://dx.doi.org/10.1049/iet-syb.2017.0019 |
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author | Siebert, Sabrina Ickstadt, Katja Schäfer, Martin Radon, Yvonne Verveer, Peter J. |
author_facet | Siebert, Sabrina Ickstadt, Katja Schäfer, Martin Radon, Yvonne Verveer, Peter J. |
author_sort | Siebert, Sabrina |
collection | PubMed |
description | Cells communicate with their environment via proteins, located at the plasma membrane separating the interior of a cell from its surroundings. The spatial distribution of these proteins in the plasma membrane under different physiological conditions is of importance, since this may influence their signal transmission properties. In this study, the authors compare different methods such as hierarchical clustering, extensible Markov models and the gammics method for analysing such a spatial distribution. The methods are examined in a simulation study to determine their optimal use. Afterwards, they analyse experimental imaging data and extend these methods to simulate dual colour data. |
format | Online Article Text |
id | pubmed-8687232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Institution of Engineering and Technology |
record_format | MEDLINE/PubMed |
spelling | pubmed-86872322022-02-16 Comparison of clustering approaches with application to dual colour protein data Siebert, Sabrina Ickstadt, Katja Schäfer, Martin Radon, Yvonne Verveer, Peter J. IET Syst Biol Research Article Cells communicate with their environment via proteins, located at the plasma membrane separating the interior of a cell from its surroundings. The spatial distribution of these proteins in the plasma membrane under different physiological conditions is of importance, since this may influence their signal transmission properties. In this study, the authors compare different methods such as hierarchical clustering, extensible Markov models and the gammics method for analysing such a spatial distribution. The methods are examined in a simulation study to determine their optimal use. Afterwards, they analyse experimental imaging data and extend these methods to simulate dual colour data. The Institution of Engineering and Technology 2018-02-01 /pmc/articles/PMC8687232/ /pubmed/29337285 http://dx.doi.org/10.1049/iet-syb.2017.0019 Text en © 2020 The Institution of Engineering and Technology https://creativecommons.org/licenses/by-nc-nd/3.0/This is an open access article published by the IET under the Creative Commons Attribution‐NonCommercial‐NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/ (https://creativecommons.org/licenses/by-nc-nd/3.0/) ) |
spellingShingle | Research Article Siebert, Sabrina Ickstadt, Katja Schäfer, Martin Radon, Yvonne Verveer, Peter J. Comparison of clustering approaches with application to dual colour protein data |
title | Comparison of clustering approaches with application to dual colour protein data |
title_full | Comparison of clustering approaches with application to dual colour protein data |
title_fullStr | Comparison of clustering approaches with application to dual colour protein data |
title_full_unstemmed | Comparison of clustering approaches with application to dual colour protein data |
title_short | Comparison of clustering approaches with application to dual colour protein data |
title_sort | comparison of clustering approaches with application to dual colour protein data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687232/ https://www.ncbi.nlm.nih.gov/pubmed/29337285 http://dx.doi.org/10.1049/iet-syb.2017.0019 |
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